Large Scale and Big Data: Processing and Management

Sherif Sakr, Mohamed Gaber

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$95.96

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June 25, 2014 by Auerbach Publications
Professional - 636 Pages - 255 B/W Illustrations
ISBN 9781466581500 - CAT# K18876

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Features

  • Examines cloud data management architectures
  • Covers Big Data analytics and visualization
  • Considers data management and analytics for vast amounts of unstructured data
  • Explores clustering, classification, and link analysis of Big Data
  • Reviews scalable data mining and machine learning techniques

Summary

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.

The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security.

Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing.

After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.